Biochemical indexes and gut microbiota testing as diagnostic methods for Penaeus monodon health and physiological changes during AHPND infection with food safety concerns

Abstract Severe shrimp disease outbreaks have a destructive impact on shrimp aquaculture and its associated downstream food processing industries. Thus, it is essential to develop proper methods for shrimp disease control, which emphasizes the importance of food safety. In this study, we performed biochemical tests and gut microbiome analysis using uninfected control and Vp AHPND‐infected Penaeus monodon samples. Biochemical tests were performed to assess the phenoloxidase (PO) activity, respiratory Burst (RB) activity, nitrite concentration, superoxide dismutase (SOD) activity, total hemocyte count (THC), and total protein concentrations. Overall, upregulations were detected in these biochemical tests, which showed the activation of the immune response in P. monodon during acute hepatopancreatic necrosis disease (AHPND) infection, especially at 6 hpi and 12 hpi. Besides that, shrimp gut samples were collected and pooled (n = 3), followed by DNA extraction, PCR amplification targeting the V3/V4 16S ribosomal RNA (rRNA) region, next‐generation sequencing (NGS), and bioinformatics analysis. Proteobacteria was the most abundant phylum in both samples. The Rhodobacteraceae family and Maritimibacter genus were proposed to be vital forshrimp health maintenance. Vp AHPND bacterial colonization and secondary Vibrio infections were postulated to have occurred based on the higher abundances of Vibrionaceae family and Vibrio genus in the Vp AHPND‐infected sample. Firmicutes phylum together with Photobacterium and Aliiroseovarius genera were inferred to be pathogenic or related factors of AHPND infections. In conclusion, physiology (immune response activation) and gut microbiome changes of disease tolerant P. monodon during AHPND infection were identified. Both biochemical tests and 16S rRNA analysis are proposed as a combined strategy for shrimp health diagnosis for ensuring shrimp health maintenance, disease control, and food safety.

globally (Bondad-Reantaso et al., 2008), there had been insufficient studies on shrimp diseases from the perspective of food safety. The majority of shrimp-related food safety studies were focused on dietary supplementation (Li et al., 2007) or the food processing (Kaur et al., 2013). Seafood-borne illnesses are majorly caused by Vibrio species, especially V. parahaemolyticus, V. cholerae, and V. vulnificus (Bondad-Reantaso et al., 2008;Gopal et al., 2005). The bacterial infections that are potentially pathogenic to humans may have occurred in diseased aquatic animals, particularly those caused by Vibrios (Austin, 2010). In a previous study, Gopal et al. (2005) showed that even though not all isolated Vibrio species possessed pathogenicity or toxicity traits, a significant percentage composition of these Vibrio species found and isolated from the farmed shrimp samples clearly exhibited hidden risks associated with food safety in shrimp consumption. Such hidden risks can be eliminated by ensuring proper shrimp health detection and shrimp gut microbiome monitoring.
The correct and accurate determination of shrimp physiological changes is vital for shrimp health detection and disease prevention. Physiological changes in shrimps in response to stress or diseases are usually detected through various biochemical tests.
Other than that, another essential strategy for shrimp health diagnosis and disease prevention would be shrimp gut microbiome analysis. This is because of the crucial roles played by gut microbiota in the various physiological processes, including metabolism (Tremaroli & Bäckhed, 2012), immune regulation (Maynard et al., 2012), endocrine function (Clarke et al., 2014), and pathogen elimination (Endt et al., 2010). Ever since the widescale application of high throughput next-generation sequencing (NGS) technology, there had been increasing demands of NGS application in gut microbiome analysis especially involving 16S rRNA diversity due to cost and effectiveness concerns (Caporaso et al., 2011). 16S rRNA studies are usually conducted for the identification of microbial diversity related to host or environmental parameters (Marzinelli et al., 2015) and pattern of gene content (Konstantinidis & Tiedje, 2005). The V3/ V4 hypervariable region of the 16S rRNA gene is commonly targeted for 16S rRNA sequencing analysis, as demonstrated in some previous works (Fan et al., 2019;Porchas-Cornejo et al., 2017;Zoqratt et al., 2018). An advantage of 16S rRNA sequencing analysis would be its less reliance on the quality of extracted DNA samples (Rintala et al., 2017). Although in fewer frequencies, there were also some publications of 16S rRNA analysis involving healthy and diseased shrimps (Cornejo-Granados et al., 2017;Liang et al., 2020;Zhou et al., 2019).
Therefore, this study aims to identify the physiology and gut microbiome changes of P. monodon during AHPND infection. The biochemical tests and 16S rRNA sequencing technique are also proposed as a combined enhanced strategy for the diagnosis of shrimp disease and the regulation of shrimp health, from the perspective of food safety and nutrition.

| Shrimp pathogenic challenge and sample collection
Juvenile disease tolerant crossbred P. monodon shrimps (5th generation Malaysian strain crossed with 13th generation Madagascar strain) with an average body length of 15-20 cm were collected from a local commercial farm. A modified immersion method (Tran et al., 2013) based AHPND bacterial challenge experiment was conducted as described previously (Devadas, 2019;Soo et al., 2019).
For the AHPND experimental challenge, Vp AHPND bacteria (KS17.S5-1 strain) at a concentration of 2 × 10 6 cfu/ml was used for the Vp AHPND -infected treatment group, whereas sterile TSB + broth was used for the uninfected control treatment group.
27 shrimps were placed in each tank filled with aerated artificial seawater (30 ppt) at 28 ± 1.0°C under an aseptic setup. Shrimp acclimatization was performed for 7 days before the challenge experiment. Three shrimp samples from each treatment group were collected at 0, 3, 6, 12, 24, 36, and 48 h post-infection (hpi) and their vital organs (hepatopancreas, gut, muscle, and haemolymph) were stored at −80°C. The AHPND infection was validated by the observation of gross clinical symptoms (pale white hepatopancreas, empty stomach, and empty gut) and the AP3 PCR detection method (Sirikharin et al., 2015). The Ethical approval for this work was granted by the University of Malaya (Ethical Application Ref: S/31012019/26112018-05/R).

| Biochemical test validation
Hepatopancreas from the collected P. monodon samples were homogenized in 1X phosphate-buffered saline (PBS) at a ratio of 1:9 and centrifuged at 13,300 g for 20 min at 4°C. The supernatant was collected from homogenized samples, diluted based on downstream applications, and stored at −20°C. The collected shrimp muscle and hemolymph samples were also utilized for biochemical analysis. The biochemical experiments were performed in a Greiner 96-well U bottom microplate (Greiner Bio-One).
All biochemical assay experiments were conducted with three biological replicates and three technical replicates for each. Statistical analysis involving one-way analysis of variance (ANOVA) accompanied by Duncan's post hoc test was performed. The statistical significance value was set at p < .05. The raw data for the biochemical assay experiments was provided in Data S1.

| Bradford protein assay
Hepatopancreas and muscle samples of P. monodon were homogenized (mixed with 1X PBS at the ratio of 1:9 in ice water) and centrifuged (580 g for 10 min), and the supernatant was collection (diluted as and when needed) and utilized for the Bradford protein assay experiment. The total protein concentrations were identified by the Bradford Assay method (Bradford, 1976) with slight modifications.
For the modified Bradford protein estimation assay, 5 µl of homogenized sample supernatant was mixed with 250 µl of 1X Bradford reagent (Coomassie brilliant blue G-250 dissolved in methanol, phosphoric acid, and water) and was subsequently incubated for 50 min at room temperature. Then, the absorbance of the solution was measured at 595 nm, using a Tecan M200 Infinite Pro Microplate Reader (Tecan Group). The positive control used was bovine serum albumin (BSA) solution (for standard curve plotting), whereas the negative control used was 1X PBS.

| Phenoloxidase activity assay
Phenoloxidase activity assay was done by a previously described modified method (Hong et al., 2019;Park et al., 2019).

| Respiratory burst activity assay
Respiratory burst activity assay was carried out based on a previously described method (Huynh et al., 2011) with slight modifications. The 96-well microplate was coated with 100 µl of poly-L-lysine solution (0.2% v/v) (diluted with deionized water) 24 h before use for increased cell adhesion. 50 µl of diluted hemolymph samples were mixed with 100 µl of Zymosan A (1 mg/ml) in modified complete Hank's balanced salt solution (MCHBSS) and the mixture was left to react for 30 min at room temperature. 100 µl of nitro blue tetrazolium chloride (NBT) solution (0.3% w/v) was then added to the mixture and it was incubated for 30 min at room temperature.
Then, 50 µl of 100% methanol was added to stop the reaction. The mixture was discarded, and the microplate was washed thrice with 100 µl of 70% methanol and air-dried for 30 min. 120 µl of potassium hydroxide (KOH) (2 M) (dissolved in deionized water) and 140 µl of dimethyl sulfoxide (DMSO) were added for dissolving the insoluble formazan crystals formed from NBT reduction. The RB activity was measured at 630 nm using the Tecan M200 Infinite Pro Microplate Reader (Tecan Group) as the measurement of superoxide anion generation. Zymosan A solution in MCHBSS was used as the positive control, whereas 1X PBS was used as the negative control.

| Relative superoxide dismutase activity assay
The relative SOD activity was measured based on a method described by Perera et al. (2017) with slight modifications. Initially, the reaction mixture containing 20 µl of homogenized hepatopancreas sample supernatant or 1X PBS (negative control), 160 µl of glycine-NaOH buffer (pH 9; 0.1 M) (dissolved in deionized water), and 6.75 µl of each: ethylenediaminetetraacetic acid (EDTA) (3 mM) (dissolved in deionized water), 0.15% BSA, xanthine (3 mM) (dissolved in deionized water), and NBT (0.75 mM) (dissolved in deionized water) was prepared. The reaction mixture was incubated for 10 min at room temperature. The reaction was then initiated by adding 6 mU of xanthine oxidase (dissolved in deionized water) and allowed to run for 20 min at room temperature. The SOD activity was measured at 560 nm every two mins for a total period of 20 min, using the Tecan M200 Infinite Pro Microplate Reader (Tecan Group). The relative SOD activity was calculated as the percentage of respective SOD enzyme activity from the highest SOD enzyme activity (100%).

| Nitrite concentration assay
The nitrite concentration measurement was estimated by a modi-

| Total hemocyte count
The THC was obtained using a modified standard method as described previously (Huynh et al., 2018). Hemolymph samples were mixed with 0.5% trypan blue solution (diluted with deionized water) at a dilution ratio of 1:1. The Neubauer improved hemocytometer (Marienfeld) and its coverslip were wiped with 70% ethanol before usage. The coverslip was set onto the hemocytometer, and 10 µl of diluted hemolymph mixture was filled into one of the chambers of the hemocytometer. The cells were counted under 40× magnification using Leica DM750 upright microscope (Leica Microsystems).

| Bioinformatics analysis and statistical validation
The quality of the raw data sequences was checked using FastQC software (Andrews, 2010) and Usearch (Edgar, 2010) software. The sequences were then merged and trimmed using Vsearch software (Rognes et al., 2016) and Trimmomatic software (Bolger et al., 2014) (Average quality score of 20, 50 bp minimum length, 50 bp sliding window). The trimmed sequences obtained were clustered into operational taxonomic units (OTUs) using UPARSE software (Edgar, 2013) with a 97% similarity cut-off. The chimera sequences were removed using Usearch software (Edgar, 2010). The 16S rRNA OTUs obtained were taxonomically classified using RDP Classifier  by matching against Silva (SSU123) 16S rRNA database (confidence threshold of 0.7). The microbial communities of the sequenced samples were plotted in bar charts at phylum, family, and genus levels. Analysis with the Linear discriminant analysis effect size (LEfSe) tool was carried out using OTUs obtained to identify the differentially abundant bacterial taxa up to the genus level (LDA cutoff value: 5.0 or higher) (Segata et al., 2011). The alpha diversity parameters, including Good's coverage, Chao 1 estimator, Shannon index, and Simpson index, were calculated using Mothur software (Schloss et al., 2009). The alpha diversity parameters (Chao 1 estimator, Shannon index, and Simpson index) were further plotted as box plot diagrams using R software (Team RC, 2013). Rarefaction and Shannon rarefaction curves were determined and plotted using Mothur software (Schloss et al., 2009) and R software (Team RC, 2013). The beta diversity parameters, including Bray-Curtis dissimilarity, Euclidean distance, Jaccard coefficient, and Manhattan distance were also calculated and plotted as PCOA diagrams using Usearch software (Edgar, 2010) and R software (Team RC, 2013).
The raw data sequences (healthy and infected samples) of previous

F I G U R E 1 Overall pathogenic and toxin flow of AHPND infection in Penaeus monodon
studies (Foysal et al., 2021;Hossain et al., 2021) were retrieved from NCBI database (BioProject ID: PRJNA662111; PRJNA662500) and used in the beta diversity analysis for cross comparison purpose. A Venn diagram was plotted using Mothur software (Schloss et al., 2009) and R software (Team RC, 2013

| Detection of activated immune response through biochemical tests
Similar to previous work (Pan et al., 2008), the activated immune response of disease tolerant P. monodon during AHPND infection can be detected through suitable key immune parameters such as biochemical-related ones. In this study, several biochemical tests including, PO activity assay, RB activity assay, nitrite concentration assay, relative SOD activity assay, and THC, were carried out and they successfully showed the differential biochemical activities associated with immune response activation of P. monodon during AHPND infection. The biochemical test results are presented in Table 1

| Overall comparison of gut microbiota diversity (relative abundance) of uninfected control and Vp AHPND -infected shrimps
The microbiota diversity or relative abundance of OTUs (phylum, family, and genus levels) between uninfected control and Vp AHPNDinfected P. monodon gut samples were determined by 16S rRNA sequencing analysis as shown in Figure 4a-

| Immune response activation during AHPND infection
In this study, a series of biochemical tests ( Table 1)  The upregulation of PO activity led to a stronger melanization response as melanization is controlled by the phenoloxidase enzyme activity in the prophenoloxidase (proPO) activation system (Alvarez & Chung, 2013;Amparyup et al., 2013;Hong et al., 2019;Park et al., 2019). There was a similar case of PO activity increment in M. rosenbergii after bacterial infection (Sung et al., 2004). The upregulation of RB and SOD activities inferred an elevated level of superoxide anion that caused increased antioxidation activity during AHPND infection. The elevation of nitrite concentration can trigger the activation of metabolic and immune reactions. In addition, nitrite formation in the redox reaction between nitric oxide and reactive oxygen species is vital for the elimination of pathogen through phagocytosis (Wink et al., 2011). However, excessively high nitrite concentration could cause adverse effects, including suppressed immune response, increased cytotoxic level, elevated susceptibility to bacterial infection, and increased superoxide anion level (Tseng & Chen, 2004).
The occurrence of hemocyte depletion due to apoptotic activity was also inferred through the decrease of THC levels. A similar scenario would be the occurrence of hemocyte depletion in Drosophila due to apoptotic activity that caused pro-inflammatory state formation and alteration of the immune effector pathway (Arefin et al., 2015). Besides that, the general reduction of total protein concentrations in Vp AHPND -infected P. monodon muscle samples, especially at 36 hpi, inferred protein degradation in shrimp muscle and probable muscle disturbance or degradation during AHPND infection. The inference was done based on a similar previous study that showed reduced total protein concentrations in both muscle and hepatopancreas of WSSV-infected Penaeus indicus (Yoganandhan et al., 2003).  (Tayag et al., 2010) or suppressed  immunity. As shown in a previous Litopenaeus vannamei challenge using Vibrio harveyi (Huang et al., 2013), despite the common reduction of SOD activity, THC, and PO activity during early infection time points, increased levels of SOD activity, THC, and PO activity were detected in disease-resistant shrimps compared to non-resistant shrimps.
The elevated biochemical measurements were also accompanied by a faster recovery rate (Huang et al., 2013). Other than that, the immune humoral parameters involved, such as THC, PO, and NBT reduction, had been previously proposed as good potential stress indicators for aquatic animal health status (Verghese et al., 2007).
Hence, this suggests the suitability of the biochemical parameters tested in this study as indicators of shrimp health status and immune response activation during pathogenic infection.

| Gut microbiome changes during AHPND infection
Based on the alpha diversity parameters obtained (Figure 2 and Table S8), Good's coverage values of more than 0.99 (99%) identified for both uninfected control and Vp AHPND -infected samples suggested satisfactory sequencing depth (Shin et al., 2019) and thus able to represent all bacterial communities. The higher alpha diversity parameter values determined in the Vp AHPND -infected sample compared to the uninfected control sample showed stronger bacterial richness and diversity caused by AHPND infection. These alpha diversity parameters included Chao 1 richness estimation (Chao, 1984;Colwell & Coddington, 1994;Gotelli & Colwell, 2011) together with Shannon (Shannon, 1948) and Simpson (Simpson, 1949) diversity indices.
The plateau stage was reached as the number of reads increased in the rarefaction ( Figure S10) and Shannon rarefaction ( Figure S11) curves plotted, which showed good sequencing depth and coverage (Liu, Yang, et al., 2020;Zhang et al., 2020). In general, the sequencing depth, coverage, richness, and diversity of the 16S data were successfully validated through all the alpha diversity parameters calculated.
From the microbiome relative abundances compared between uninfected control and Vp AHPND -infected gut samples (Figure 4), the Proteobacteria phylum was most abundantly found in both samples.  et al., 2017). Notably, the Firmicutes phylum, which had a near 28-fold relative abundance increment from the uninfected control sample to Vp AHPND -infected sample, can be suggested as a potential determinant factor in the colonization or infection process of Vp AHPND bacteria. Firmicutes bacteria can survive in different environments, including extreme conditions, and produce endospores (Galperin, 2016). Firmicutes bacteria was previously reported to have elevated gut relative abundance in V. alginolyticus-infected crab (Shi et al., 2019) and a positive correlation of its gut relative abundance with immune gene expressions in dietary supplemented Fenneropenaeus merguiensis (Liu, Zhou, et al., 2020).
The there are insufficient studies on this genus by which only two species were found at the current moment (Lee et al., 2007;Zhong et al., 2015).
In addition, the Vibrionaceae family and Vibrio genus showed higher abundances in the Vp AHPND -infected sample compared to the uninfected control sample. These higher abundances were postulated to be caused by Vp AHPND bacterial colonization and related to secondary Vibrio infections. The inhabitation of Vibrio spp. bacteria in the shrimp intestine was due to its chitin-rich environment identical to other crustaceans (Sugita & Ito, 2006). The secondary bacterial infections that occurred were mentioned previously (Santos et al., 2020). The Photobacterium genus bacteria had the highest relative abundance in the Vp AHPND -infected sample that may be correlated to secondary luminous bacterial infections (Prayitno & Latchford, 1995). Aliiroseovarius genus can be proposed as pathogenic bacteria related to Vibrio bacteria based on a previous decrement of relative abundances for Vibrio and Aliiroseovarius genera bacteria in L. vannamei treated with beneficial seaweed feeding (Elizondo-González et al., 2020).
In the process of AHPND infection, Vp AHPND bacteria started to colonize the shrimp's stomach after entry through the oral route. The bacteria then released PirA and PirB toxins, which led to damaging of the shrimp hepatopancreas. There was also the identification of both Vp AHPND bacteria and its toxins in the shrimp hepatopancreas during later post-infection time points (Lai et al., 2015). Additionally, the occurrence of Vp AHPND bacterial colonization and hemocytic infiltration at the shrimp anterior midgut were detected during post-AHPND infection time points (Soonthornchai et al., 2016). The Vp AHPND bacterial colonization locations were also shown by the common use of shrimp digestive organs such as stomach, hepatopancreas, midgut, and hindgut in AHPND diagnosis (Zorriehzahra & Banaederakhshan, 2015).
Gut microbiota is vital in the gut immune response such that disrupted microbiota will lead to immune dysregulation (Round & Mazmanian, 2009). A balanced gut microbiome composition is crucial in disease control (Biesebeke, 2018;Buttó & Haller, 2016). The shrimp gut microbiota dysbiosis is correlated to disease severity, which also involves environmental stress factors (Xiong et al., 2015).

| Diagnostic and food safety applications
In this study, biochemical tests and 16S rRNA analysis are suggested as suitable diagnostic tools for the determination of shrimp health status and gut microbiome changes, especially between healthy and diseased shrimps. Such applications are vital for ensuring food safety in downstream consumption. Some of the commonly investigated biochemical aspects of food safety include biochemical lesions, enzyme inhibition, and congenital metabolic disorders (Walker, 1980).
Biochemical tests were utilized in food safety studies, such as bacteria biochemical tests (ALatawi et al., 2015) and immunological biochemical tests (Sun et al., 2011). The common applications of immunological biochemical tests in food safety-related studies would be probiotics or dietary supplementation works (Gupta et al., 2014;Kumar et al., 2013;Sun et al., 2011), challenged shrimp works (Vaseeharan et al., 2013), and combined works (Citarasu et al., 2006;Gholamhosseini et al., 2020). However, there had been a lack of attention and effort in connecting immunological biochemical tests of shrimp challenge works to food safety-oriented applications.
On the other hand, due to the time and labor constraints of traditional food microbiology detection methods (Rodríguez-Lázaro et al., 2007), PCR had risen to become the standard rapid detection method for food microbes (Hameed et al., 2018). 16S rRNA analysis either through conventional PCR (ALatawi et al., 2015) or real-time qPCR (Wolffs et al., 2004) had been previously utilized in food safety studies. Furthermore, the importance of cultured environment bacterial composition and associated shrimp gut microbiota changes was successfully highlighted from the beneficial changes in growth, immune response, survival, and gut microbiome of probiotics-supplemented P. indicus cultured under biofloc system (Panigrahi et al., 2020). Such importance suggests the necessity of 16S rRNA analysis to be applied in the diagnosis of shrimp health and detection of shrimp disease outbreaks. The shrimp health diagnosis can be achieved through the close comparison of shrimp gut relative abundances, particularly focusing on potentially pathogenic or disease-related microbes such as Vibrio genera.

| CON CLUS ION
In conclusion, the biochemical tests performed in this study, includ- proposed as a combined strategy to be applied in ensuring shrimp health status diagnosis and disease control. The successful application of such strategies can then lead to stronger food safety and nutrition starting from the beginning of the food processing chain.
Shrimp diseases are potentially accompanied by pathogens that are harmful to humans. Healthy shrimps not only are safer for consumption but also possess higher nutrition values compared to diseased shrimps.
Based on the results obtained, enhanced biochemical tests can be developed to achieve cost-effective and efficient detection of shrimp health status changes, followed by the establishment of a biochemical-based profiling system. The important beneficiary shrimp gut microbial communities identified in this study can assist in the development of enhanced shrimp supplements to achieve better shrimp health conditions and stronger disease resistance or tolerance. Other than that, an associated shrimp gut microbial profiling system can also be established for shrimp health diagnosis, disease detection, disease severity estimation (Dai et al., 2020;Xiong et al., 2015), and identification of potential polymicrobial infections (Dai et al., 2018).

ACK N OWLED G EM ENTS
This work was supported by the Technofund grant from the Ministry of Science and Technology, Malaysia (PV019-2016) and the Centre for Research in Biotechnology for Agriculture (CEBAR) grant (RU004G-2020). The authors acknowledge the prawn/shrimp farmers that helped them in providing the shrimps and fellow AGAGEL lab members.

CO N FLI C T O F I NTE R E S T
No conflict of interest is declared.